A Novel Bitline Repeater For High Reliable Nonvolatile RRAM-based FPGA Configuration Memory
Nonvolatile SRAM (NVSRAM) has garnered significant attention over the past few years for its integration of high-speed write/read capabilities of SRAM and the nonvolatile cells. However, research on integrating NVSRAM into radiation-hardened systems, which demand high reliability, remains limited. FPGA is key components in space electronic systems, with their configuration memory playing a vital role in enabling programmable logic, and its ability to resist interference is crucial. While NVSRAM based on RRAM can significantly enhance the interference resistance, the write currents are considerably higher than SRAM, leading to a pronounced IR drop on the bitline, and it can easily cause write failures as parallelism increases. To address the issues, this study proposes a novel bitline repeater for RRAM-based FPGA configuration memory. By connecting in parallel on the bitline, the impact of IR drop can be significantly mitigated, leading to a potential multiplication in the number of configuration memory cells. The simulation results demonstrate that the repeater circuit can resist high-energy particles interference with the charge exceeding 100pC, which corresponds to a Linear Energy Transfer (LET) threshold exceeding 10000 MeV·cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>/mg.
Authors
- Bowen Ding (ORCID: https://orcid.org/0000-0003-2456-1729)
- Lei Chen (ORCID: https://orcid.org/0000-0002-4778-4688)
- Meiran Zhao (ORCID: https://orcid.org/0000-0002-2932-088X)
- Yimao Cai (ORCID: https://orcid.org/0000-0002-6854-8211)
- Yujia Li (ORCID: https://orcid.org/0000-0001-8988-2254)
- Rui Guo (ORCID: https://orcid.org/0000-0001-8415-3570)
- Huabo Sun
Institutions
- Peking University (CN)
- Beijing Microelectronics Technology Institute (CN)
Publication Details
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1109/icicm63644.2024.10814130
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China